共查询到19条相似文献,搜索用时 62 毫秒
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不确定多输入非线性系统自适应模糊滑模控制器设计 总被引:2,自引:0,他引:2
针对一类不确定多输入非线性系统提出一种新的自适应模糊滑模控制器,该控制器在存在模型逻辑系统逼近误差的情况下使闭环系统跟踪误差小于预先给定常数,消除滑模控制中的抖振,缓解因系统维数增高所致的模糊规则爆炸现象,最后用仿算例验证了所提出方法的有效性。 相似文献
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针对一类输入饱和不确定Brunovsky标准型非线性时滞系统,提出一种周期自适应跟踪补偿学习算法. 利用信号置换思想重组系统,基于最小公倍周期函数变换,将时滞时变项和不确定项合并为辅助参数,进而设计周期自适应学习律估计该辅助量,并利用饱和补偿器逼近和补偿超出饱和限的部分,由此构成综合控制器,以保证系统状态对有界期望值的跟踪,解决了饱和输入周期系统的重复迭代学习控制问题. 最后通过构造Lyapunov-Krasovskii复合能量函数的差分,计算证明了系统跟踪误差的收敛性和闭环信号值的有界性. 常见耦合非线性机械臂系统的力矩控制仿真,进一步验证了该算法的有效性. 相似文献
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考虑输入约束的半主动悬架非线性自适应控制 总被引:1,自引:0,他引:1
针对具有输入约束及参数不确定性问题的汽车半主动悬架系统,提出一种考虑输入饱和的非线性自适应Backstepping控制器.该方法引入一个辅助系统,通过设计新的误差变量,实现对控制饱和的补偿,解决控制输入的幅值约束问题.同时,考虑到悬架系统的参数不确定性问题,采用映射自适应算法设计自适应律,通过构造适当的Lyapunov函数,保证悬架系统的稳定性.仿真结果表明,所设计的控制器具有良好的隔振性能,而且能够有效降低输入约束和不确定参数对系统性能的影响. 相似文献
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将一类具有输入饱和的严格反馈单输入单输出非线性系统作为研究对象,解决其自适应渐近跟踪控制问题.与已有结果不同,所考虑的虚拟控制参数可以是未知且增益函数的上界信息也是未知的,这给控制器的设计带来了挑战.通过结合光滑函数及有界估计方法,设计一种新颖的自适应渐近跟踪控制策略;其次,通过引入Nassbaum函数解决由输入饱和不确定参数以及未知虚拟控制参数带来的影响;此外,通过利用未知增益的下界信息巧妙地构造一个特殊的李雅普诺夫函数并结合不等式技巧,可以消除对控制增益函数上界信息的需要,并保证系统的全局稳定性和跟踪性能;最后,通过实例仿真及对比仿真表明所提出自适应渐近跟踪控制算法的有效性. 相似文献
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非线性离散时间系统的自适应模糊补偿控制 总被引:1,自引:0,他引:1
针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制方案.控制律由跟踪控制律和逼近误差补偿控制律两部分组成,利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律实现对模糊逻辑系统逼近误差的进一步补偿.所设计的控制器可保证闭环系统一致最终有界.将该控制器用于月球探测车动态转向系统中,仿真结果表明了该方法的有效性. 相似文献
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针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制新方法。其控制律由跟踪控制律和逼近误差补偿控制律两部分组成。利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律来实现对模糊逻辑系统逼近误差的进一步补偿。所设计的控制器可保证闭环系统一致最终有界。 相似文献
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针对一类具有未知不确定性,且状态不可测的非线性系统,考虑了输入端的饱和非对称扇区非线性特性影响,提出了系统模型未知情形下基于自适应模糊观测器的跟踪控制方案,采用Lyapunov-Krasovskii函数给出了滑模控制器参数和模糊逻辑的自适应调整律.所提方法不仅可保证闭环跟踪系统的稳定性,还削弱了传统方法对模型结构的依赖... 相似文献
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针对一类输入受限的非线性系统,提出了一种自适应模糊backsteppig控制器的设计方法.在控制器的设计过程当中,采用模糊系统对不确定非线性函数在线逼近;利用双曲正切函数和Nussbaum函数对系统输入饱和函数进行处理;将动态面法与backstepping法相结合解决"计算膨胀"的问题.通过Lyapunov理论分析证明了所设计的控制器能够使闭环系统所有信号半全局一致有界(SGUUB).最后应用于高超声速飞行器的攻角跟踪控制中,仿真结果表明该方法的有效性. 相似文献
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Shengfeng Zhou Mou Chen Chong-Jin Ong Peter C. Y. Chen 《Neural computing & applications》2016,27(5):1317-1325
In this paper, an adaptive neural network (NN) tracking controller is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with input saturation. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions in the MIMO system. A novel auxiliary system is developed to compensate the effects induced by input saturation (in both magnitude and rate) during tracking control. Endowed with a switching structure that integrates two existing representative auxiliary system designs, this novel auxiliary system improves control performance by preserving their advantages. It provides a comprehensive design structure in which parameters can be adjusted to meet the required control performance. The auxiliary system signal is utilized in both the control law and the neural network weight-update laws. The performance of the resultant closed-loop system is analyzed, and the bound of the transient error is established. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive neural network control. 相似文献
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Jiali Ma Shengyuan Xu Guangming Zhuang Yunliang Wei Zhengqiang Zhang 《国际强度与非线性控制杂志
》2020,30(7):2593-2610
》2020,30(7):2593-2610
In this article, the adaptive tracking control problem is considered for a class of uncertain nonlinear systems with input delay and saturation. To compensate for the effect of the input delay and saturation, a compensation system is designed. Radial basis function neural networks are directly utilized to approximate the unknown nonlinear functions. With the aid of the backstepping method, novel adaptive neural network tracking controllers are developed, which can guarantee all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the system output can track the desired signal with a small tracking error. In the end, a simulation example is given to illustrate the effectiveness of the proposed methods. 相似文献
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Lu Senkui Li Xiang Lu Ke Wang Zhengzhong Ma Yujie 《Neural computing & applications》2023,35(11):8157-8170
Neural Computing and Applications - In this paper, an adaptive fuzzy control approach for incommensurate fractional-order multi-input multi-output (MIMO) systems with unknown nonlinearities and... 相似文献
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针对一类具有未知输入齿隙、参数不确定以及未建模动态和干扰的非线性系统,设计了自适应鲁棒控制器.将齿隙非线性模型等价表示为具有有界建模误差的全局线性化模型,在此基础上设计了包含自适应模型补偿、反馈稳定和鲁棒反馈3部分的自适应鲁棒控制器,并给出了系统动态跟踪误差和稳态误差指标.理论分析证明,闭环控制系统信号有界且跟踪误差在任意期望的精度范围内,仿真研究验证了所提出方法的有效性. 相似文献
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利用神经网络和滑模控制,研究带有饱和输入的一类非线性系统。为了便于问题分析,引入饱和约束模型输出与控制输入的差值这个变量,分5种情况讨论,求得神经网络权值的在线调节律,得到保证闭环系统稳定的控制律。利用Lyapunov函数,证明了闭环系统的稳定性;仿真实验说明了算法的有效性。 相似文献
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In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme. 相似文献
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In this article, the issue of developing an adaptive event‐triggered neural control for nonlinear uncertain system with input delay is investigated. The radial basis function neural networks (RBFNNs) are adopted to approximate the uncertain terms, where the time‐varying approximation errors are considered into the approximation system. However, the RBFNNs' weight vector is extended, which may cause the computing burdens. To save network resource, the computing burden caused by the weight vector is handled with the developed adaptive control strategy. Furthermore, in order to compensate the effect of input delay, an auxiliary system is introduced into codesign. With the help of adaptive backstepping technique, an adaptive event‐triggered control approach is established. Under the proposed control approach, the effect of input delay can be compensated effectively while the considered system suffered network resource constraint, and all signals in the close‐loop system can be guarantee bounded. Finally, two simulation examples are given to verify the proposed control method's effectiveness. 相似文献